Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study.

Journal: BMC pregnancy and childbirth

PMID: 33706701

Abstract

BACKGROUND: Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features.

Authors

Toktam Khatibi

Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 1411713116, Iran. Electronic address: toktam.khatibi@modares.ac.ir.
Elham Hanifi

School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, 14117-13114, Iran.
Mohammad Mehdi Sepehri

Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 1411713114, Iran. Electronic address: mehdi.sepehri@modares.ac.ir.
Leila Allahqoli

Endometriosis Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran.

Keywords

Adult Algorithms Body Height Cross-Sectional Studies Female Gestational Age Humans Iran Live Birth Machine Learning Pregnancy Prenatal Care Prenatal Diagnosis Prognosis Reproducibility of Results Risk Assessment Stillbirth

External Resources

View on PubMed Access via DOI PubMed (33706701)

Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study.

Abstract

Authors

Keywords

External Resources

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